High-Speed Online MPC Based on a Fast Gradient Method Applied to Power Converter Control

Bounding the computational complexity of an
online optimization method in a real-time environment with
hard time constraints is a challenging problem. This paper
investigates a new solution approach based on a fast gradient method in the context of model predictive control (MPC) of power converters. Different from other solution methods that either provide bounds that are far off from the practically observed ones or do not allow for bounding the computational effort at all this method enables easy to compute and meaningful bounds that can further be decreased by means of a preconditioning technique. We report an implementation of the fast gradient method on an industrial-type digital signal processor with integer arithmetics and show that worst case runtimes are
in the order of tens of microseconds using less than one kByte of memory while being numerically robust. Moreover, this method also improves the control performance compared to explicit MPC.